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Journal of Zhejiang University-Science C(Computers and Electronics) 2011年第8期12卷 687-697页

作者： Sun Hee KIM Hyung Jeong YANG Kam Swee NG Department of Computer Science Chonnam National University

Missing values occur in bio-signal processing for various reasons,including technical problems or biological *** missing values are then either simply excluded or substituted with estimated values for further *** the missing signal values are estimated for electroencephalography (EEG) signals,an example where electrical signals arrive quickly and successively,rapid processing of high-speed data is required for immediate decision *** this study,we propose an incremental expectation maximization principal component analysis (iEMPCA) method that automatically estimates missing values from multivariable EEG time series data without requiring a whole and complete data *** proposed method solves the problem of a biased model,which inevitably results from simply removing incomplete data rather than estimating them,and thus reduces the loss of information by incorporating missing values in real *** using an incremental approach,the proposed method alsominimizes memory usage and processing time of continuously arriving *** results show that the proposed method assigns more accurate missing values than previous methods.

关键词： Electroencephalography (EEG) Missing value imputation Hidden pattern discovery expectation maximization Principal component analysis

Detecting Bit Transition from weak GPS signals by expectation-maximization Algorithm

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Detecting Bit Transition from weak GPS signals by Expectatio...

Online belief propagation algorithm for probabilistic latent semantic analysis

第三届中国卫星导航学术年会

作者： Ao Peng 1 , Jianghong Shi 2 , Shijun Lin 3 School of Information Science and Technology, Xiamen University, Xiamen, Fujian, China

An expectation-maximization based Algorithm is proposed to detect bit transitions from weak Global Positioning System （GPS） signals. The 20ms bit duration boundary in coherent integration of received GPS signals can be broken with detected bit transitions. The impact of Doppler frequency estimation error produced by carrier tracking mechanism is removed by the proposed algorithm. The implementation of the proposed algorithm is simple so that it can be applied to a low-cost GPS receiver.

关键词： expectation maximization weak GPS signals bit transition detection

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Frontiers of Computer Science 2013年第4期7卷 526-535页

作者： Yun YE Shengrong GONG Chunping LIU Jia ZENG Ning JIA YiZHANG School of Computer Science & Technology Soochow University Suzhou 215006 China Feng Chao Revenue Baidu Online Network Technology Co. LTD Beijing 100000 China

Probabilistic latent semantic analysis （PLSA） is a topic model for text documents, which has been widely used in text mining, computer vision, computational biology and so on. For batch PLSA inference algorithms, the required memory size grows linearly with the data size, and handling massive data streams is very difficult. To process big data streams, we propose an online belief propagation （OBP） algorithm based on the improved factor graph representation for PLSA. The factor graph of PLSA facilitates the classic belief propagation （BP） algorithm. Furthermore, OBP splits the data stream into a set of small segments, and uses the estimated parameters of previous segments to calculate the gradient descent of the current segment. Because OBP removes each segment from memory after processing, it is memoryefficient for big data streams. We examine the performance of OBP on four document data sets, and demonstrate that OBP is competitive in both speed and accuracy for online ex- pectation maximization （OEM） in PLSA, and can also give a more accurate topic evolution. Experiments on massive data streams from Baidu further confirm the effectiveness of the OBP algorithm.

关键词： probabilistic latent semantic analysis topicmodels expectation maximization belief propagation

Software sensor for slab reheating furnace

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Journal of University of Science and Technology Beijing 2005年第2期12卷 123-127页

作者： ZhihuaXiong GuohongHuang HuiheShao InstituteofAutomation ShanghaiJiaotongUniversityShanghai200030China

It has long been thought that a reheating furnace, with its inherent measurement difficulties and complex dynamics, posed almost insurmountable problems to engineers in steel plants. A novel software sensor is proposed to make more effective use of those measurements that are already available, which has great importance both to slab quality and energy saving. The proposed method is based on the mixtures of Gaussian processes (GP) with the expectation maximization (EM) algorithm employed for parameter esti- mation of the mixture of models. The mixture model can alleviate the computational complexity of GP and also accords with the changes of operating condition in practical processes. It is demonstrated by on-line estimation of the furnace gas temperature in 1580 reheating furnace in Baosteel Corporation (Group).

关键词： Gaussian processes expectation maximization multiple models soft sensor reheating furnace

Research on Time Synchronization Method Under Arbitrary Network Delay in Wireless Sensor Networks

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Computers, Materials & Continua 2019年第9期61卷 1323-1344页

作者： Bing Hu Feng Xiang Fan Wu Jian Liu Zhe Sun Zhixin Sun Key Laboratory of Broadband Wireless Communication and Sensor Network Technology Nanjing University of Posts and TelecommunicationsNanjing210003China YuanTong Express Co.LTD Shanghai201705China Department of Computer Science Tuskegee UniversityTuskegeeAL36088USA College of Information Engineering Nanjing University of Finance and EconomicsNanjing210023China

To cope with the arbitrariness of the network delays,a novel method,referred to as the composite particle filter approach based on variational Bayesian(VB-CPF),is proposed herein to estimate the clock skew and clock offset in wireless sensor ***-CPF is an improvement of the Gaussian mixture kalman particle filter(GMKPF)*** GMKPF,expectation-maximization(EM)algorithm needs to determine the number of mixture components in advance,and it is easy to generate overfitting and *** Bayesian EM(VB-EM)algorithm is introduced in this paper to determine the number of mixture components adaptively according to the ***,to solve the problem of data packet loss caused by unreliable links,we propose a robust time synchronization(RTS)method in this *** establishes an autoregressive model for clock skew,and calculates the clock parameters based on the established autoregressive model in case of packet *** final simulation results illustrate that VB-CPF yields much more accurate results relative to GMKPF when the network delays are modeled in terms of an asymmetric Gaussian ***,RTS shows good robustness to the continuous and random dropout of time messages.

关键词： Time synchronization particle filter expectation maximization wireless sensor networks(WSNs)

Fusing PLSA model and Markov random fields for automatic image annotation

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High Technology Letters 2014年第4期20卷 409-414页

作者：田东平 Zhao Xiaofei Shi Zhongzhi Institute of Computer Software Baoji University of Arts and Sciences Key Laboratory of Intelligent Information Processing Institute of Computing TechnologyChinese Academy of Sciences

A novel image auto-annotation method is presented based on probabilistic latent semantic analysis(PLSA) model and multiple Markov random fields(MRF).A PLSA model with asymmetric modalities is first constructed to estimate the joint probability between images and semantic concepts,then a subgraph is extracted served as the corresponding structure of Markov random fields and inference over it is performed by the iterative conditional modes so as to capture the final annotation for the *** novelty of our method mainly lies in two aspects:exploiting PLSA to estimate the joint probability between images and semantic concepts as well as multiple MRF to further explore the semantic context among keywords for accurate image *** demonstrate the effectiveness of this approach,an experiment on the Corel5 k dataset is conducted and its results are compared favorably with the current state-of-the-art approaches.

关键词： automatic image annotation probabilistic latent semantic analysis (PLSA) expectation maximization Markov random fields (MRF) image retrieval

Study on the Development and Implementation of Different Big Data Clustering Methods

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Open Journal of Applied Sciences 2023年第7期13卷 1163-1177页

作者： Jean Pierre Ntayagabiri Jérémie Ndikumagenge Longin Ndayisaba Boribo Kikunda Philippe Center of Research in Infrastructure Environment and Technology (CRIET) University of Burundi Bujumbura Burundi Catholic University of Bukavu Bukavu Democratic Republic of the Congo

Clustering is an unsupervised learning method used to organize raw data in such a way that those with the same (similar) characteristics are found in the same class and those that are dissimilar are found in different classes. In this day and age, the very rapid increase in the amount of data being produced brings new challenges in the analysis and storage of this data. Recently, there is a growing interest in key areas such as real-time data mining, which reveal an urgent need to process very large data under strict performance constraints. The objective of this paper is to survey four algorithms including K-Means algorithm, FCM algorithm, EM algorithm and BIRCH, used for data clustering and then show their strengths and weaknesses. Another task is to compare the results obtained by applying each of these algorithms to the same data and to give a conclusion based on these results.

关键词： Clustering K-Means Fuzzy c-Means expectation maximization BIRCH

Oriented Particle Filter for Visual Tracking

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Oriented Particle Filter for Visual Tracking